Overview

Brought to you by YData

Dataset statistics

Number of variables35
Number of observations4424
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory915.8 KiB
Average record size in memory212.0 B

Variable types

Categorical18
Numeric17

Alerts

Application mode is highly overall correlated with InternationalHigh correlation
Course is highly overall correlated with Daytime/evening attendanceHigh correlation
Curricular units 1st sem (approved) is highly overall correlated with Curricular units 1st sem (enrolled) and 4 other fieldsHigh correlation
Curricular units 1st sem (credited) is highly overall correlated with Curricular units 2nd sem (credited)High correlation
Curricular units 1st sem (enrolled) is highly overall correlated with Curricular units 1st sem (approved) and 2 other fieldsHigh correlation
Curricular units 1st sem (evaluations) is highly overall correlated with Curricular units 2nd sem (evaluations)High correlation
Curricular units 1st sem (grade) is highly overall correlated with Curricular units 1st sem (approved) and 2 other fieldsHigh correlation
Curricular units 2nd sem (approved) is highly overall correlated with Curricular units 1st sem (approved) and 5 other fieldsHigh correlation
Curricular units 2nd sem (credited) is highly overall correlated with Curricular units 1st sem (credited)High correlation
Curricular units 2nd sem (enrolled) is highly overall correlated with Curricular units 1st sem (approved) and 2 other fieldsHigh correlation
Curricular units 2nd sem (evaluations) is highly overall correlated with Curricular units 1st sem (evaluations)High correlation
Curricular units 2nd sem (grade) is highly overall correlated with Curricular units 1st sem (approved) and 2 other fieldsHigh correlation
Daytime/evening attendance is highly overall correlated with CourseHigh correlation
Father's occupation is highly overall correlated with Mother's occupationHigh correlation
International is highly overall correlated with Application mode and 1 other fieldsHigh correlation
Mother's occupation is highly overall correlated with Father's occupationHigh correlation
Nacionality is highly overall correlated with InternationalHigh correlation
Target is highly overall correlated with Curricular units 2nd sem (approved)High correlation
Marital status is highly imbalanced (75.3%) Imbalance
Daytime/evening attendance is highly imbalanced (50.3%) Imbalance
Previous qualification is highly imbalanced (73.4%) Imbalance
Nacionality is highly imbalanced (94.3%) Imbalance
Educational special needs is highly imbalanced (90.9%) Imbalance
International is highly imbalanced (83.2%) Imbalance
Curricular units 1st sem (credited) has 3847 (87.0%) zeros Zeros
Curricular units 1st sem (enrolled) has 180 (4.1%) zeros Zeros
Curricular units 1st sem (evaluations) has 349 (7.9%) zeros Zeros
Curricular units 1st sem (approved) has 718 (16.2%) zeros Zeros
Curricular units 1st sem (grade) has 718 (16.2%) zeros Zeros
Curricular units 1st sem (without evaluations) has 4130 (93.4%) zeros Zeros
Curricular units 2nd sem (credited) has 3894 (88.0%) zeros Zeros
Curricular units 2nd sem (enrolled) has 180 (4.1%) zeros Zeros
Curricular units 2nd sem (evaluations) has 401 (9.1%) zeros Zeros
Curricular units 2nd sem (approved) has 870 (19.7%) zeros Zeros
Curricular units 2nd sem (grade) has 870 (19.7%) zeros Zeros
Curricular units 2nd sem (without evaluations) has 4142 (93.6%) zeros Zeros

Reproduction

Analysis started2025-03-23 02:02:43.470533
Analysis finished2025-03-23 02:03:12.757861
Duration29.29 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

Marital status
Categorical

Imbalance 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Single
3919 
Married
 
379
Divorced
 
91
Facto union
 
25
Legally separated
 
6

Length

Max length17
Median length6
Mean length6.1708861
Min length6

Characters and Unicode

Total characters27300
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowSingle
4th rowSingle
5th rowMarried

Common Values

ValueCountFrequency (%)
Single 3919
88.6%
Married 379
 
8.6%
Divorced 91
 
2.1%
Facto union 25
 
0.6%
Legally separated 6
 
0.1%
Widower 4
 
0.1%

Length

2025-03-23T03:03:12.817733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T03:03:12.893052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
single 3919
88.0%
married 379
 
8.5%
divorced 91
 
2.0%
facto 25
 
0.6%
union 25
 
0.6%
legally 6
 
0.1%
separated 6
 
0.1%
widower 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 4418
16.2%
e 4411
16.2%
n 3969
14.5%
l 3931
14.4%
g 3925
14.4%
S 3919
14.4%
r 859
 
3.1%
d 480
 
1.8%
a 422
 
1.5%
M 379
 
1.4%
Other values (14) 587
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4418
16.2%
e 4411
16.2%
n 3969
14.5%
l 3931
14.4%
g 3925
14.4%
S 3919
14.4%
r 859
 
3.1%
d 480
 
1.8%
a 422
 
1.5%
M 379
 
1.4%
Other values (14) 587
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4418
16.2%
e 4411
16.2%
n 3969
14.5%
l 3931
14.4%
g 3925
14.4%
S 3919
14.4%
r 859
 
3.1%
d 480
 
1.8%
a 422
 
1.5%
M 379
 
1.4%
Other values (14) 587
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4418
16.2%
e 4411
16.2%
n 3969
14.5%
l 3931
14.4%
g 3925
14.4%
S 3919
14.4%
r 859
 
3.1%
d 480
 
1.8%
a 422
 
1.5%
M 379
 
1.4%
Other values (14) 587
 
2.2%

Application mode
Categorical

High correlation 

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
1st phase - general contingent
1708 
2nd phase - general contingent
872 
Over 23 years old
785 
Change of course
312 
Technological specialization diploma holders
213 
Other values (13)
534 

Length

Max length51
Median length30
Mean length27.182866
Min length8

Characters and Unicode

Total characters120257
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row2nd phase - general contingent
2nd rowInternational student (bachelor)
3rd row1st phase - general contingent
4th row2nd phase - general contingent
5th rowOver 23 years old

Common Values

ValueCountFrequency (%)
1st phase - general contingent 1708
38.6%
2nd phase - general contingent 872
19.7%
Over 23 years old 785
17.7%
Change of course 312
 
7.1%
Technological specialization diploma holders 213
 
4.8%
Holders of other higher courses 139
 
3.1%
3rd phase - general contingent 124
 
2.8%
Transfer 77
 
1.7%
Change of institution/course 59
 
1.3%
1st phase - special contingent (Madeira Island) 38
 
0.9%
Other values (8) 97
 
2.2%

Length

2025-03-23T03:03:12.986276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2758
13.7%
contingent 2758
13.7%
phase 2758
13.7%
general 2704
13.5%
1st 1762
8.8%
2nd 872
 
4.3%
over 785
 
3.9%
23 785
 
3.9%
years 785
 
3.9%
old 785
 
3.9%
Other values (33) 3310
16.5%

Most occurring characters

ValueCountFrequency (%)
15638
13.0%
e 14799
12.3%
n 13056
10.9%
t 7974
 
6.6%
a 7875
 
6.5%
s 6847
 
5.7%
g 6186
 
5.1%
o 6166
 
5.1%
r 5895
 
4.9%
l 4968
 
4.1%
Other values (38) 30853
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 120257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15638
13.0%
e 14799
12.3%
n 13056
10.9%
t 7974
 
6.6%
a 7875
 
6.5%
s 6847
 
5.7%
g 6186
 
5.1%
o 6166
 
5.1%
r 5895
 
4.9%
l 4968
 
4.1%
Other values (38) 30853
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 120257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15638
13.0%
e 14799
12.3%
n 13056
10.9%
t 7974
 
6.6%
a 7875
 
6.5%
s 6847
 
5.7%
g 6186
 
5.1%
o 6166
 
5.1%
r 5895
 
4.9%
l 4968
 
4.1%
Other values (38) 30853
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 120257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15638
13.0%
e 14799
12.3%
n 13056
10.9%
t 7974
 
6.6%
a 7875
 
6.5%
s 6847
 
5.7%
g 6186
 
5.1%
o 6166
 
5.1%
r 5895
 
4.9%
l 4968
 
4.1%
Other values (38) 30853
25.7%

Application order
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7278481
Minimum0
Maximum9
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:13.055928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3137931
Coefficient of variation (CV)0.76036376
Kurtosis2.6512887
Mean1.7278481
Median Absolute Deviation (MAD)0
Skewness1.88105
Sum7644
Variance1.7260523
MonotonicityNot monotonic
2025-03-23T03:03:13.118994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 3026
68.4%
2 547
 
12.4%
3 309
 
7.0%
4 249
 
5.6%
5 154
 
3.5%
6 137
 
3.1%
9 1
 
< 0.1%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 3026
68.4%
2 547
 
12.4%
3 309
 
7.0%
4 249
 
5.6%
5 154
 
3.5%
6 137
 
3.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
6 137
 
3.1%
5 154
 
3.5%
4 249
 
5.6%
3 309
 
7.0%
2 547
 
12.4%
1 3026
68.4%
0 1
 
< 0.1%

Course
Categorical

High correlation 

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
Nursing
766 
Management
380 
Social Service
355 
Veterinary Nursing
337 
Journalism and Communication
331 
Other values (12)
2255 

Length

Max length36
Median length28
Mean length17.961573
Min length7

Characters and Unicode

Total characters79462
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnimation and Multimedia Design
2nd rowTourism
3rd rowCommunication Design
4th rowJournalism and Communication
5th rowSocial Service (evening attendance)

Common Values

ValueCountFrequency (%)
Nursing 766
17.3%
Management 380
 
8.6%
Social Service 355
 
8.0%
Veterinary Nursing 337
 
7.6%
Journalism and Communication 331
 
7.5%
Management (evening attendance) 268
 
6.1%
Advertising and Marketing Management 268
 
6.1%
Tourism 252
 
5.7%
Communication Design 226
 
5.1%
Animation and Multimedia Design 215
 
4.9%
Other values (7) 1026
23.2%

Length

2025-03-23T03:03:13.206926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nursing 1103
 
12.1%
management 916
 
10.1%
and 814
 
8.9%
social 570
 
6.3%
service 570
 
6.3%
communication 557
 
6.1%
evening 483
 
5.3%
attendance 483
 
5.3%
design 441
 
4.8%
veterinary 337
 
3.7%
Other values (17) 2832
31.1%

Most occurring characters

ValueCountFrequency (%)
n 10203
12.8%
i 8022
 
10.1%
e 7459
 
9.4%
a 6745
 
8.5%
4682
 
5.9%
t 4257
 
5.4%
r 4255
 
5.4%
g 4127
 
5.2%
m 3423
 
4.3%
o 3324
 
4.2%
Other values (28) 22965
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 10203
12.8%
i 8022
 
10.1%
e 7459
 
9.4%
a 6745
 
8.5%
4682
 
5.9%
t 4257
 
5.4%
r 4255
 
5.4%
g 4127
 
5.2%
m 3423
 
4.3%
o 3324
 
4.2%
Other values (28) 22965
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 10203
12.8%
i 8022
 
10.1%
e 7459
 
9.4%
a 6745
 
8.5%
4682
 
5.9%
t 4257
 
5.4%
r 4255
 
5.4%
g 4127
 
5.2%
m 3423
 
4.3%
o 3324
 
4.2%
Other values (28) 22965
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 10203
12.8%
i 8022
 
10.1%
e 7459
 
9.4%
a 6745
 
8.5%
4682
 
5.9%
t 4257
 
5.4%
r 4255
 
5.4%
g 4127
 
5.2%
m 3423
 
4.3%
o 3324
 
4.2%
Other values (28) 22965
28.9%

Daytime/evening attendance
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
Daytime
3941 
Evening
483 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters30968
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDaytime
2nd rowDaytime
3rd rowDaytime
4th rowDaytime
5th rowEvening

Common Values

ValueCountFrequency (%)
Daytime 3941
89.1%
Evening 483
 
10.9%

Length

2025-03-23T03:03:13.284395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T03:03:13.332148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
daytime 3941
89.1%
evening 483
 
10.9%

Most occurring characters

ValueCountFrequency (%)
i 4424
14.3%
e 4424
14.3%
D 3941
12.7%
a 3941
12.7%
y 3941
12.7%
t 3941
12.7%
m 3941
12.7%
n 966
 
3.1%
E 483
 
1.6%
v 483
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4424
14.3%
e 4424
14.3%
D 3941
12.7%
a 3941
12.7%
y 3941
12.7%
t 3941
12.7%
m 3941
12.7%
n 966
 
3.1%
E 483
 
1.6%
v 483
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4424
14.3%
e 4424
14.3%
D 3941
12.7%
a 3941
12.7%
y 3941
12.7%
t 3941
12.7%
m 3941
12.7%
n 966
 
3.1%
E 483
 
1.6%
v 483
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4424
14.3%
e 4424
14.3%
D 3941
12.7%
a 3941
12.7%
y 3941
12.7%
t 3941
12.7%
m 3941
12.7%
n 966
 
3.1%
E 483
 
1.6%
v 483
 
1.6%

Previous qualification
Categorical

Imbalance 

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
Secondary education
3717 
Technological specialization course
 
219
Basic education 3rd cycle (9th/10th/11th year) or equiv.
 
162
Higher education - Degree
 
126
Other - 11th year of schooling
 
45
Other values (12)
 
155

Length

Max length56
Median length19
Mean length22.025316
Min length19

Characters and Unicode

Total characters97440
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowSecondary education
2nd rowSecondary education
3rd rowSecondary education
4th rowSecondary education
5th rowSecondary education

Common Values

ValueCountFrequency (%)
Secondary education 3717
84.0%
Technological specialization course 219
 
5.0%
Basic education 3rd cycle (9th/10th/11th year) or equiv. 162
 
3.7%
Higher education - Degree 126
 
2.8%
Other - 11th year of schooling 45
 
1.0%
Higher education - Degree (1st cycle) 40
 
0.9%
Professional higher technical course 36
 
0.8%
Higher education - Bachelor's Degree 23
 
0.5%
Frequency of higher education 16
 
0.4%
12th year of schooling - Not completed 11
 
0.2%
Other values (7) 29
 
0.7%

Length

2025-03-23T03:03:13.414033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
education 4106
37.4%
secondary 3717
33.9%
266
 
2.4%
higher 256
 
2.3%
course 255
 
2.3%
year 232
 
2.1%
technological 219
 
2.0%
specialization 219
 
2.0%
cycle 215
 
2.0%
degree 189
 
1.7%
Other values (23) 1301
 
11.9%

Most occurring characters

ValueCountFrequency (%)
e 10176
10.4%
c 9526
9.8%
o 9240
9.5%
a 8991
9.2%
n 8425
8.6%
d 8015
 
8.2%
6551
 
6.7%
i 5711
 
5.9%
r 5115
 
5.2%
t 5066
 
5.2%
Other values (36) 20624
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10176
10.4%
c 9526
9.8%
o 9240
9.5%
a 8991
9.2%
n 8425
8.6%
d 8015
 
8.2%
6551
 
6.7%
i 5711
 
5.9%
r 5115
 
5.2%
t 5066
 
5.2%
Other values (36) 20624
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10176
10.4%
c 9526
9.8%
o 9240
9.5%
a 8991
9.2%
n 8425
8.6%
d 8015
 
8.2%
6551
 
6.7%
i 5711
 
5.9%
r 5115
 
5.2%
t 5066
 
5.2%
Other values (36) 20624
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10176
10.4%
c 9526
9.8%
o 9240
9.5%
a 8991
9.2%
n 8425
8.6%
d 8015
 
8.2%
6551
 
6.7%
i 5711
 
5.9%
r 5115
 
5.2%
t 5066
 
5.2%
Other values (36) 20624
21.2%

Nacionality
Categorical

High correlation  Imbalance 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
Portuguese
4314 
Brazilian
 
38
Santomean
 
14
Cape Verdean
 
13
Spanish
 
13
Other values (16)
 
32

Length

Max length21
Median length10
Mean length9.9760398
Min length5

Characters and Unicode

Total characters44134
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st rowPortuguese
2nd rowPortuguese
3rd rowPortuguese
4th rowPortuguese
5th rowPortuguese

Common Values

ValueCountFrequency (%)
Portuguese 4314
97.5%
Brazilian 38
 
0.9%
Santomean 14
 
0.3%
Cape Verdean 13
 
0.3%
Spanish 13
 
0.3%
Guinean 5
 
0.1%
Ukrainian 3
 
0.1%
Moldova (Republic of) 3
 
0.1%
Italian 3
 
0.1%
Russian 2
 
< 0.1%
Other values (11) 16
 
0.4%

Length

2025-03-23T03:03:13.512275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
portuguese 4314
97.1%
brazilian 38
 
0.9%
santomean 14
 
0.3%
cape 13
 
0.3%
verdean 13
 
0.3%
spanish 13
 
0.3%
guinean 5
 
0.1%
ukrainian 3
 
0.1%
moldova 3
 
0.1%
republic 3
 
0.1%
Other values (14) 24
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 8693
19.7%
u 8642
19.6%
r 4371
9.9%
o 4345
9.8%
t 4333
9.8%
s 4333
9.8%
g 4317
9.8%
P 4314
9.8%
a 183
 
0.4%
n 132
 
0.3%
Other values (30) 471
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44134
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8693
19.7%
u 8642
19.6%
r 4371
9.9%
o 4345
9.8%
t 4333
9.8%
s 4333
9.8%
g 4317
9.8%
P 4314
9.8%
a 183
 
0.4%
n 132
 
0.3%
Other values (30) 471
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44134
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8693
19.7%
u 8642
19.6%
r 4371
9.9%
o 4345
9.8%
t 4333
9.8%
s 4333
9.8%
g 4317
9.8%
P 4314
9.8%
a 183
 
0.4%
n 132
 
0.3%
Other values (30) 471
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44134
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8693
19.7%
u 8642
19.6%
r 4371
9.9%
o 4345
9.8%
t 4333
9.8%
s 4333
9.8%
g 4317
9.8%
P 4314
9.8%
a 183
 
0.4%
n 132
 
0.3%
Other values (30) 471
 
1.1%
Distinct29
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Secondary Education - 12th Year of Schooling or Eq.
1069 
Basic education 1st cycle (4th/5th year) or equiv.
1009 
Basic Education 3rd Cycle (9th/10th/11th Year) or Equiv.
953 
Basic Education 2nd Cycle (6th/7th/8th Year) or Equiv.
562 
Higher Education - Degree
438 
Other values (24)
393 

Length

Max length56
Median length51
Mean length47.232143
Min length7

Characters and Unicode

Total characters208955
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowBasic Education 3rd Cycle (9th/10th/11th Year) or Equiv.
2nd rowSecondary Education - 12th Year of Schooling or Eq.
3rd rowBasic education 1st cycle (4th/5th year) or equiv.
4th rowBasic Education 2nd Cycle (6th/7th/8th Year) or Equiv.
5th rowBasic education 1st cycle (4th/5th year) or equiv.

Common Values

ValueCountFrequency (%)
Secondary Education - 12th Year of Schooling or Eq. 1069
24.2%
Basic education 1st cycle (4th/5th year) or equiv. 1009
22.8%
Basic Education 3rd Cycle (9th/10th/11th Year) or Equiv. 953
21.5%
Basic Education 2nd Cycle (6th/7th/8th Year) or Equiv. 562
12.7%
Higher Education - Degree 438
9.9%
Unknown 130
 
2.9%
Higher Education - Bachelor's Degree 83
 
1.9%
Higher Education - Master's 49
 
1.1%
Other - 11th Year of Schooling 42
 
0.9%
Higher Education - Doctorate 21
 
0.5%
Other values (19) 68
 
1.5%

Length

2025-03-23T03:03:13.605057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
education 4202
12.7%
year 3661
11.1%
or 3596
10.9%
cycle 2539
 
7.7%
equiv 2524
 
7.7%
basic 2524
 
7.7%
1730
 
5.2%
of 1139
 
3.5%
schooling 1134
 
3.4%
12th 1077
 
3.3%
Other values (46) 8863
26.9%

Most occurring characters

ValueCountFrequency (%)
28565
 
13.7%
t 13118
 
6.3%
c 12665
 
6.1%
o 12620
 
6.0%
e 11822
 
5.7%
a 11674
 
5.6%
i 11073
 
5.3%
r 10671
 
5.1%
h 9611
 
4.6%
n 7403
 
3.5%
Other values (45) 79733
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 208955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
28565
 
13.7%
t 13118
 
6.3%
c 12665
 
6.1%
o 12620
 
6.0%
e 11822
 
5.7%
a 11674
 
5.6%
i 11073
 
5.3%
r 10671
 
5.1%
h 9611
 
4.6%
n 7403
 
3.5%
Other values (45) 79733
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 208955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
28565
 
13.7%
t 13118
 
6.3%
c 12665
 
6.1%
o 12620
 
6.0%
e 11822
 
5.7%
a 11674
 
5.6%
i 11073
 
5.3%
r 10671
 
5.1%
h 9611
 
4.6%
n 7403
 
3.5%
Other values (45) 79733
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 208955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
28565
 
13.7%
t 13118
 
6.3%
c 12665
 
6.1%
o 12620
 
6.0%
e 11822
 
5.7%
a 11674
 
5.6%
i 11073
 
5.3%
r 10671
 
5.1%
h 9611
 
4.6%
n 7403
 
3.5%
Other values (45) 79733
38.2%
Distinct34
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Basic education 1st cycle (4th/5th year) or equiv.
1209 
Basic Education 3rd Cycle (9th/10th/11th Year) or Equiv.
968 
Secondary Education - 12th Year of Schooling or Eq.
904 
Basic Education 2nd Cycle (6th/7th/8th Year) or Equiv.
702 
Higher Education - Degree
282 
Other values (29)
359 

Length

Max length56
Median length51
Mean length48.450271
Min length7

Characters and Unicode

Total characters214344
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.2%

Sample

1st rowOther - 11th Year of Schooling
2nd rowHigher Education - Degree
3rd rowBasic education 1st cycle (4th/5th year) or equiv.
4th rowBasic education 1st cycle (4th/5th year) or equiv.
5th rowBasic Education 2nd Cycle (6th/7th/8th Year) or Equiv.

Common Values

ValueCountFrequency (%)
Basic education 1st cycle (4th/5th year) or equiv. 1209
27.3%
Basic Education 3rd Cycle (9th/10th/11th Year) or Equiv. 968
21.9%
Secondary Education - 12th Year of Schooling or Eq. 904
20.4%
Basic Education 2nd Cycle (6th/7th/8th Year) or Equiv. 702
15.9%
Higher Education - Degree 282
 
6.4%
Unknown 112
 
2.5%
Higher Education - Bachelor's Degree 68
 
1.5%
Higher Education - Master's 39
 
0.9%
Other - 11th Year of Schooling 38
 
0.9%
Technological specialization course 20
 
0.5%
Other values (24) 82
 
1.9%

Length

2025-03-23T03:03:13.712712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
education 4200
12.5%
year 3860
11.5%
or 3785
11.3%
cycle 2888
 
8.6%
basic 2879
 
8.6%
equiv 2879
 
8.6%
1368
 
4.1%
1st 1214
 
3.6%
4th/5th 1209
 
3.6%
of 974
 
2.9%
Other values (52) 8342
24.8%

Most occurring characters

ValueCountFrequency (%)
29174
 
13.6%
t 14013
 
6.5%
c 13262
 
6.2%
o 12170
 
5.7%
a 12090
 
5.6%
e 11871
 
5.5%
i 11473
 
5.4%
r 10522
 
4.9%
h 9959
 
4.6%
n 7203
 
3.4%
Other values (46) 82607
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 214344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29174
 
13.6%
t 14013
 
6.5%
c 13262
 
6.2%
o 12170
 
5.7%
a 12090
 
5.6%
e 11871
 
5.5%
i 11473
 
5.4%
r 10522
 
4.9%
h 9959
 
4.6%
n 7203
 
3.4%
Other values (46) 82607
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 214344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29174
 
13.6%
t 14013
 
6.5%
c 13262
 
6.2%
o 12170
 
5.7%
a 12090
 
5.6%
e 11871
 
5.5%
i 11473
 
5.4%
r 10522
 
4.9%
h 9959
 
4.6%
n 7203
 
3.4%
Other values (46) 82607
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 214344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29174
 
13.6%
t 14013
 
6.5%
c 13262
 
6.2%
o 12170
 
5.7%
a 12090
 
5.6%
e 11871
 
5.5%
i 11473
 
5.4%
r 10522
 
4.9%
h 9959
 
4.6%
n 7203
 
3.4%
Other values (46) 82607
38.5%

Mother's occupation
Categorical

High correlation 

Distinct32
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Unskilled Workers
1577 
Administrative staff
817 
Personal Services, Security and Safety Workers and Sellers
530 
Intermediate Level Technicians and Professions
351 
Specialists in Intellectual and Scientific Activities
318 
Other values (27)
831 

Length

Max length106
Median length96
Mean length33.185353
Min length7

Characters and Unicode

Total characters146812
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowPersonal Services, Security and Safety Workers and Sellers
2nd rowIntermediate Level Technicians and Professions
3rd rowUnskilled Workers
4th rowPersonal Services, Security and Safety Workers and Sellers
5th rowUnskilled Workers

Common Values

ValueCountFrequency (%)
Unskilled Workers 1577
35.6%
Administrative staff 817
18.5%
Personal Services, Security and Safety Workers and Sellers 530
 
12.0%
Intermediate Level Technicians and Professions 351
 
7.9%
Specialists in Intellectual and Scientific Activities 318
 
7.2%
Skilled Workers in Industry, Construction and Craftsmen 272
 
6.1%
Student 144
 
3.3%
Representatives of the Legislative Power and Executive Bodies, Directors, Directors and Executive Managers 102
 
2.3%
Farmers and Skilled Workers in Agriculture, Fisheries and Forestry 91
 
2.1%
Other Situation 70
 
1.6%
Other values (22) 152
 
3.4%

Length

2025-03-23T03:03:13.821153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
workers 2561
 
14.4%
and 2503
 
14.1%
unskilled 1586
 
8.9%
administrative 823
 
4.6%
staff 823
 
4.6%
in 705
 
4.0%
services 537
 
3.0%
personal 535
 
3.0%
sellers 532
 
3.0%
security 530
 
3.0%
Other values (85) 6648
37.4%

Most occurring characters

ValueCountFrequency (%)
e 16091
 
11.0%
13359
 
9.1%
s 12033
 
8.2%
i 11945
 
8.1%
r 11330
 
7.7%
n 10033
 
6.8%
t 9042
 
6.2%
a 7803
 
5.3%
l 7569
 
5.2%
d 6193
 
4.2%
Other values (36) 41414
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 146812
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 16091
 
11.0%
13359
 
9.1%
s 12033
 
8.2%
i 11945
 
8.1%
r 11330
 
7.7%
n 10033
 
6.8%
t 9042
 
6.2%
a 7803
 
5.3%
l 7569
 
5.2%
d 6193
 
4.2%
Other values (36) 41414
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 146812
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 16091
 
11.0%
13359
 
9.1%
s 12033
 
8.2%
i 11945
 
8.1%
r 11330
 
7.7%
n 10033
 
6.8%
t 9042
 
6.2%
a 7803
 
5.3%
l 7569
 
5.2%
d 6193
 
4.2%
Other values (36) 41414
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 146812
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 16091
 
11.0%
13359
 
9.1%
s 12033
 
8.2%
i 11945
 
8.1%
r 11330
 
7.7%
n 10033
 
6.8%
t 9042
 
6.2%
a 7803
 
5.3%
l 7569
 
5.2%
d 6193
 
4.2%
Other values (36) 41414
28.2%

Father's occupation
Categorical

High correlation 

Distinct46
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Unskilled Workers
1010 
Skilled Workers in Industry, Construction and Craftsmen
666 
Personal Services, Security and Safety Workers and Sellers
516 
Administrative staff
386 
Intermediate Level Technicians and Professions
384 
Other values (41)
1462 

Length

Max length106
Median length88
Mean length40.826627
Min length7

Characters and Unicode

Total characters180617
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.3%

Sample

1st rowUnskilled Workers
2nd rowIntermediate Level Technicians and Professions
3rd rowUnskilled Workers
4th rowIntermediate Level Technicians and Professions
5th rowUnskilled Workers

Common Values

ValueCountFrequency (%)
Unskilled Workers 1010
22.8%
Skilled Workers in Industry, Construction and Craftsmen 666
15.1%
Personal Services, Security and Safety Workers and Sellers 516
11.7%
Administrative staff 386
 
8.7%
Intermediate Level Technicians and Professions 384
 
8.7%
Installation and Machine Operators and Assembly Workers 318
 
7.2%
Armed Forces Professions 266
 
6.0%
Farmers and Skilled Workers in Agriculture, Fisheries and Forestry 242
 
5.5%
Specialists in Intellectual and Scientific Activities 197
 
4.5%
Representatives of the Legislative Power and Executive Bodies, Directors, Directors and Executive Managers 134
 
3.0%
Other values (36) 305
 
6.9%

Length

2025-03-23T03:03:13.932491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 3737
 
16.6%
workers 2795
 
12.4%
in 1136
 
5.0%
unskilled 1031
 
4.6%
skilled 920
 
4.1%
construction 689
 
3.1%
industry 681
 
3.0%
craftsmen 666
 
3.0%
professions 651
 
2.9%
services 522
 
2.3%
Other values (116) 9676
43.0%

Most occurring characters

ValueCountFrequency (%)
e 18776
 
10.4%
18080
 
10.0%
r 15292
 
8.5%
s 14798
 
8.2%
n 13361
 
7.4%
i 12191
 
6.7%
t 10122
 
5.6%
a 9731
 
5.4%
l 8084
 
4.5%
o 8041
 
4.5%
Other values (38) 52141
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180617
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 18776
 
10.4%
18080
 
10.0%
r 15292
 
8.5%
s 14798
 
8.2%
n 13361
 
7.4%
i 12191
 
6.7%
t 10122
 
5.6%
a 9731
 
5.4%
l 8084
 
4.5%
o 8041
 
4.5%
Other values (38) 52141
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180617
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 18776
 
10.4%
18080
 
10.0%
r 15292
 
8.5%
s 14798
 
8.2%
n 13361
 
7.4%
i 12191
 
6.7%
t 10122
 
5.6%
a 9731
 
5.4%
l 8084
 
4.5%
o 8041
 
4.5%
Other values (38) 52141
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180617
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 18776
 
10.4%
18080
 
10.0%
r 15292
 
8.5%
s 14798
 
8.2%
n 13361
 
7.4%
i 12191
 
6.7%
t 10122
 
5.6%
a 9731
 
5.4%
l 8084
 
4.5%
o 8041
 
4.5%
Other values (38) 52141
28.9%

Displaced
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
1
2426 
0
1998 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Length

2025-03-23T03:03:14.022137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T03:03:14.071784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Most occurring characters

ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Educational special needs
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
4373 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Length

2025-03-23T03:03:14.130838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T03:03:14.175973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Debtor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
3921 
1
503 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Length

2025-03-23T03:03:14.233850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T03:03:14.281381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
1
3896 
0
528 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Length

2025-03-23T03:03:14.339634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T03:03:14.387822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Most occurring characters

ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
2868 
1
1556 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Length

2025-03-23T03:03:14.446084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T03:03:14.492751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Most occurring characters

ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
3325 
1
1099 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Length

2025-03-23T03:03:14.554842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T03:03:14.600878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Most occurring characters

ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Age at enrollment
Real number (ℝ)

Distinct46
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.265145
Minimum17
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:14.675483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile18
Q119
median20
Q325
95-th percentile41
Maximum70
Range53
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.5878156
Coefficient of variation (CV)0.32614522
Kurtosis4.1268918
Mean23.265145
Median Absolute Deviation (MAD)2
Skewness2.0549884
Sum102925
Variance57.574946
MonotonicityNot monotonic
2025-03-23T03:03:14.779655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
18 1036
23.4%
19 911
20.6%
20 599
13.5%
21 322
 
7.3%
22 174
 
3.9%
24 131
 
3.0%
23 108
 
2.4%
26 94
 
2.1%
25 93
 
2.1%
27 91
 
2.1%
Other values (36) 865
19.6%
ValueCountFrequency (%)
17 5
 
0.1%
18 1036
23.4%
19 911
20.6%
20 599
13.5%
21 322
 
7.3%
22 174
 
3.9%
23 108
 
2.4%
24 131
 
3.0%
25 93
 
2.1%
26 94
 
2.1%
ValueCountFrequency (%)
70 1
 
< 0.1%
62 1
 
< 0.1%
61 1
 
< 0.1%
60 2
 
< 0.1%
59 3
0.1%
58 3
0.1%
57 2
 
< 0.1%
55 5
0.1%
54 7
0.2%
53 7
0.2%

International
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
0
4314 
1
 
110

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Length

2025-03-23T03:03:14.868299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T03:03:14.914362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Curricular units 1st sem (credited)
Real number (ℝ)

High correlation  Zeros 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70999096
Minimum0
Maximum20
Zeros3847
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:14.971344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3605066
Coefficient of variation (CV)3.3246995
Kurtosis19.205727
Mean0.70999096
Median Absolute Deviation (MAD)0
Skewness4.1690488
Sum3141
Variance5.5719915
MonotonicityNot monotonic
2025-03-23T03:03:15.054206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 3847
87.0%
2 94
 
2.1%
1 85
 
1.9%
3 69
 
1.6%
6 51
 
1.2%
4 47
 
1.1%
7 41
 
0.9%
5 41
 
0.9%
8 31
 
0.7%
9 27
 
0.6%
Other values (11) 91
 
2.1%
ValueCountFrequency (%)
0 3847
87.0%
1 85
 
1.9%
2 94
 
2.1%
3 69
 
1.6%
4 47
 
1.1%
5 41
 
0.9%
6 51
 
1.2%
7 41
 
0.9%
8 31
 
0.7%
9 27
 
0.6%
ValueCountFrequency (%)
20 2
 
< 0.1%
19 2
 
< 0.1%
18 4
 
0.1%
17 3
 
0.1%
16 3
 
0.1%
15 5
 
0.1%
14 15
0.3%
13 13
0.3%
12 12
0.3%
11 17
0.4%

Curricular units 1st sem (enrolled)
Real number (ℝ)

High correlation  Zeros 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2705696
Minimum0
Maximum26
Zeros180
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:15.136381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q15
median6
Q37
95-th percentile11
Maximum26
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4801782
Coefficient of variation (CV)0.39552677
Kurtosis8.9379154
Mean6.2705696
Median Absolute Deviation (MAD)1
Skewness1.6190409
Sum27741
Variance6.1512838
MonotonicityNot monotonic
2025-03-23T03:03:15.215972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6 1910
43.2%
5 1010
22.8%
7 656
 
14.8%
8 296
 
6.7%
0 180
 
4.1%
12 66
 
1.5%
10 52
 
1.2%
11 45
 
1.0%
9 36
 
0.8%
15 25
 
0.6%
Other values (13) 148
 
3.3%
ValueCountFrequency (%)
0 180
 
4.1%
1 7
 
0.2%
2 9
 
0.2%
3 10
 
0.2%
4 21
 
0.5%
5 1010
22.8%
6 1910
43.2%
7 656
 
14.8%
8 296
 
6.7%
9 36
 
0.8%
ValueCountFrequency (%)
26 1
 
< 0.1%
23 2
 
< 0.1%
21 6
 
0.1%
19 2
 
< 0.1%
18 19
0.4%
17 16
0.4%
16 13
0.3%
15 25
0.6%
14 22
0.5%
13 20
0.5%

Curricular units 1st sem (evaluations)
Real number (ℝ)

High correlation  Zeros 

Distinct35
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2990506
Minimum0
Maximum45
Zeros349
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:15.300551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q310
95-th percentile15
Maximum45
Range45
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1791056
Coefficient of variation (CV)0.50356429
Kurtosis5.4630252
Mean8.2990506
Median Absolute Deviation (MAD)2
Skewness0.9766367
Sum36715
Variance17.464923
MonotonicityNot monotonic
2025-03-23T03:03:15.389521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
8 791
17.9%
7 703
15.9%
6 598
13.5%
9 402
9.1%
0 349
7.9%
10 340
7.7%
11 239
 
5.4%
12 223
 
5.0%
5 220
 
5.0%
13 140
 
3.2%
Other values (25) 419
9.5%
ValueCountFrequency (%)
0 349
7.9%
1 6
 
0.1%
2 8
 
0.2%
3 6
 
0.1%
4 19
 
0.4%
5 220
 
5.0%
6 598
13.5%
7 703
15.9%
8 791
17.9%
9 402
9.1%
ValueCountFrequency (%)
45 2
< 0.1%
36 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
29 2
< 0.1%
28 1
 
< 0.1%
27 2
< 0.1%
26 4
0.1%
25 3
0.1%

Curricular units 1st sem (approved)
Real number (ℝ)

High correlation  Zeros 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7066004
Minimum0
Maximum26
Zeros718
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:15.600800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q36
95-th percentile9
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.094238
Coefficient of variation (CV)0.65742526
Kurtosis3.0966799
Mean4.7066004
Median Absolute Deviation (MAD)1
Skewness0.7662624
Sum20822
Variance9.5743087
MonotonicityNot monotonic
2025-03-23T03:03:15.680997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6 1171
26.5%
5 723
16.3%
0 718
16.2%
7 471
10.6%
4 433
 
9.8%
3 269
 
6.1%
2 160
 
3.6%
1 127
 
2.9%
8 108
 
2.4%
11 49
 
1.1%
Other values (13) 195
 
4.4%
ValueCountFrequency (%)
0 718
16.2%
1 127
 
2.9%
2 160
 
3.6%
3 269
 
6.1%
4 433
 
9.8%
5 723
16.3%
6 1171
26.5%
7 471
10.6%
8 108
 
2.4%
9 40
 
0.9%
ValueCountFrequency (%)
26 1
 
< 0.1%
21 4
 
0.1%
20 3
 
0.1%
19 2
 
< 0.1%
18 15
0.3%
17 10
 
0.2%
16 5
 
0.1%
15 7
 
0.2%
14 14
0.3%
13 26
0.6%

Curricular units 1st sem (grade)
Real number (ℝ)

High correlation  Zeros 

Distinct805
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.640822
Minimum0
Maximum18.875
Zeros718
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:15.775042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median12.285714
Q313.4
95-th percentile14.857143
Maximum18.875
Range18.875
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation4.8436634
Coefficient of variation (CV)0.45519637
Kurtosis0.90846103
Mean10.640822
Median Absolute Deviation (MAD)1.1571429
Skewness-1.5681456
Sum47074.995
Variance23.461075
MonotonicityNot monotonic
2025-03-23T03:03:15.887182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 718
 
16.2%
12 205
 
4.6%
13 147
 
3.3%
11 138
 
3.1%
11.5 89
 
2.0%
14 85
 
1.9%
12.5 84
 
1.9%
10 82
 
1.9%
12.66666667 82
 
1.9%
12.33333333 82
 
1.9%
Other values (795) 2712
61.3%
ValueCountFrequency (%)
0 718
16.2%
9.8 1
 
< 0.1%
10 82
 
1.9%
10.16666667 1
 
< 0.1%
10.2 8
 
0.2%
10.21428571 1
 
< 0.1%
10.25 7
 
0.2%
10.28571429 1
 
< 0.1%
10.33333333 16
 
0.4%
10.36842105 1
 
< 0.1%
ValueCountFrequency (%)
18.875 1
 
< 0.1%
18 2
 
< 0.1%
17.33333333 2
 
< 0.1%
17.125 1
 
< 0.1%
17.11111111 1
 
< 0.1%
17.00555556 1
 
< 0.1%
17 5
0.1%
16.9 1
 
< 0.1%
16.88571429 1
 
< 0.1%
16.85714286 1
 
< 0.1%
Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13765823
Minimum0
Maximum12
Zeros4130
Zeros (%)93.4%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:15.973446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69088018
Coefficient of variation (CV)5.0188078
Kurtosis89.863208
Mean0.13765823
Median Absolute Deviation (MAD)0
Skewness8.2074031
Sum609
Variance0.47731543
MonotonicityNot monotonic
2025-03-23T03:03:16.045069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 4130
93.4%
1 153
 
3.5%
2 79
 
1.8%
3 23
 
0.5%
4 15
 
0.3%
6 6
 
0.1%
7 6
 
0.1%
5 5
 
0.1%
8 4
 
0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
0 4130
93.4%
1 153
 
3.5%
2 79
 
1.8%
3 23
 
0.5%
4 15
 
0.3%
5 5
 
0.1%
6 6
 
0.1%
7 6
 
0.1%
8 4
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
12 2
 
< 0.1%
10 1
 
< 0.1%
8 4
 
0.1%
7 6
 
0.1%
6 6
 
0.1%
5 5
 
0.1%
4 15
 
0.3%
3 23
 
0.5%
2 79
1.8%
1 153
3.5%

Curricular units 2nd sem (credited)
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54181736
Minimum0
Maximum19
Zeros3894
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:16.114636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9185461
Coefficient of variation (CV)3.5409462
Kurtosis24.427266
Mean0.54181736
Median Absolute Deviation (MAD)0
Skewness4.6348195
Sum2397
Variance3.6808193
MonotonicityNot monotonic
2025-03-23T03:03:16.190710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 3894
88.0%
1 107
 
2.4%
2 92
 
2.1%
4 78
 
1.8%
5 68
 
1.5%
3 49
 
1.1%
6 26
 
0.6%
11 20
 
0.5%
7 16
 
0.4%
9 15
 
0.3%
Other values (9) 59
 
1.3%
ValueCountFrequency (%)
0 3894
88.0%
1 107
 
2.4%
2 92
 
2.1%
3 49
 
1.1%
4 78
 
1.8%
5 68
 
1.5%
6 26
 
0.6%
7 16
 
0.4%
8 12
 
0.3%
9 15
 
0.3%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 2
 
< 0.1%
16 2
 
< 0.1%
15 2
 
< 0.1%
14 4
 
0.1%
13 9
0.2%
12 14
0.3%
11 20
0.5%
10 13
0.3%
9 15
0.3%

Curricular units 2nd sem (enrolled)
Real number (ℝ)

High correlation  Zeros 

Distinct22
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2321429
Minimum0
Maximum23
Zeros180
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:16.267736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median6
Q37
95-th percentile10
Maximum23
Range23
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1959508
Coefficient of variation (CV)0.35235886
Kurtosis7.13474
Mean6.2321429
Median Absolute Deviation (MAD)1
Skewness0.7881135
Sum27571
Variance4.8221997
MonotonicityNot monotonic
2025-03-23T03:03:16.345356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
6 1913
43.2%
5 1054
23.8%
8 661
 
14.9%
7 304
 
6.9%
0 180
 
4.1%
11 60
 
1.4%
9 50
 
1.1%
10 48
 
1.1%
12 44
 
1.0%
13 37
 
0.8%
Other values (12) 73
 
1.7%
ValueCountFrequency (%)
0 180
 
4.1%
1 3
 
0.1%
2 5
 
0.1%
3 3
 
0.1%
4 17
 
0.4%
5 1054
23.8%
6 1913
43.2%
7 304
 
6.9%
8 661
 
14.9%
9 50
 
1.1%
ValueCountFrequency (%)
23 2
 
< 0.1%
21 1
 
< 0.1%
19 3
 
0.1%
18 2
 
< 0.1%
17 12
 
0.3%
16 1
 
< 0.1%
15 2
 
< 0.1%
14 22
0.5%
13 37
0.8%
12 44
1.0%

Curricular units 2nd sem (evaluations)
Real number (ℝ)

High correlation  Zeros 

Distinct30
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0632911
Minimum0
Maximum33
Zeros401
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:16.422705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q310
95-th percentile15
Maximum33
Range33
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.9479509
Coefficient of variation (CV)0.48962029
Kurtosis2.0682859
Mean8.0632911
Median Absolute Deviation (MAD)2
Skewness0.33649718
Sum35672
Variance15.586317
MonotonicityNot monotonic
2025-03-23T03:03:16.507588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
8 792
17.9%
6 614
13.9%
7 563
12.7%
9 456
10.3%
0 401
9.1%
10 355
8.0%
5 288
 
6.5%
11 255
 
5.8%
12 226
 
5.1%
13 126
 
2.8%
Other values (20) 348
7.9%
ValueCountFrequency (%)
0 401
9.1%
1 3
 
0.1%
2 4
 
0.1%
3 2
 
< 0.1%
4 10
 
0.2%
5 288
 
6.5%
6 614
13.9%
7 563
12.7%
8 792
17.9%
9 456
10.3%
ValueCountFrequency (%)
33 1
 
< 0.1%
28 1
 
< 0.1%
27 2
 
< 0.1%
26 3
 
0.1%
25 1
 
< 0.1%
24 3
 
0.1%
23 4
 
0.1%
22 10
0.2%
21 10
0.2%
20 8
0.2%

Curricular units 2nd sem (approved)
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4358047
Minimum0
Maximum20
Zeros870
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:16.586199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q36
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0147639
Coefficient of variation (CV)0.67964306
Kurtosis0.84504466
Mean4.4358047
Median Absolute Deviation (MAD)2
Skewness0.30627938
Sum19624
Variance9.0888014
MonotonicityNot monotonic
2025-03-23T03:03:16.668815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6 965
21.8%
0 870
19.7%
5 726
16.4%
4 414
9.4%
7 331
 
7.5%
8 321
 
7.3%
3 285
 
6.4%
2 198
 
4.5%
1 114
 
2.6%
11 48
 
1.1%
Other values (10) 152
 
3.4%
ValueCountFrequency (%)
0 870
19.7%
1 114
 
2.6%
2 198
 
4.5%
3 285
 
6.4%
4 414
9.4%
5 726
16.4%
6 965
21.8%
7 331
 
7.5%
8 321
 
7.3%
9 36
 
0.8%
ValueCountFrequency (%)
20 2
 
< 0.1%
19 3
 
0.1%
18 2
 
< 0.1%
17 8
 
0.2%
16 2
 
< 0.1%
14 6
 
0.1%
13 21
0.5%
12 34
0.8%
11 48
1.1%
10 38
0.9%

Curricular units 2nd sem (grade)
Real number (ℝ)

High correlation  Zeros 

Distinct786
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.230206
Minimum0
Maximum18.571429
Zeros870
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:16.767042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.75
median12.2
Q313.333333
95-th percentile14.980262
Maximum18.571429
Range18.571429
Interquartile range (IQR)2.5833333

Descriptive statistics

Standard deviation5.210808
Coefficient of variation (CV)0.50935515
Kurtosis0.066567351
Mean10.230206
Median Absolute Deviation (MAD)1.2
Skewness-1.3136502
Sum45258.43
Variance27.15252
MonotonicityNot monotonic
2025-03-23T03:03:16.874178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 870
 
19.7%
12 170
 
3.8%
11 165
 
3.7%
13 150
 
3.4%
11.5 86
 
1.9%
12.5 84
 
1.9%
14 77
 
1.7%
10 77
 
1.7%
13.5 65
 
1.5%
12.66666667 61
 
1.4%
Other values (776) 2619
59.2%
ValueCountFrequency (%)
0 870
19.7%
10 77
 
1.7%
10.16666667 4
 
0.1%
10.2 4
 
0.1%
10.25 10
 
0.2%
10.33333333 19
 
0.4%
10.375 1
 
< 0.1%
10.4 8
 
0.2%
10.42857143 2
 
< 0.1%
10.44444444 2
 
< 0.1%
ValueCountFrequency (%)
18.57142857 1
< 0.1%
17.71428571 1
< 0.1%
17.69230769 1
< 0.1%
17.6 2
< 0.1%
17.5875 1
< 0.1%
17.42857143 1
< 0.1%
17.16666667 1
< 0.1%
17 2
< 0.1%
16.90909091 1
< 0.1%
16.8 2
< 0.1%
Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15031646
Minimum0
Maximum12
Zeros4142
Zeros (%)93.6%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:16.956346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75377407
Coefficient of variation (CV)5.0145812
Kurtosis66.811692
Mean0.15031646
Median Absolute Deviation (MAD)0
Skewness7.2677009
Sum665
Variance0.56817535
MonotonicityNot monotonic
2025-03-23T03:03:17.024415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 4142
93.6%
1 140
 
3.2%
2 48
 
1.1%
3 35
 
0.8%
4 21
 
0.5%
5 17
 
0.4%
6 8
 
0.2%
8 6
 
0.1%
7 5
 
0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
0 4142
93.6%
1 140
 
3.2%
2 48
 
1.1%
3 35
 
0.8%
4 21
 
0.5%
5 17
 
0.4%
6 8
 
0.2%
7 5
 
0.1%
8 6
 
0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
12 2
 
< 0.1%
8 6
 
0.1%
7 5
 
0.1%
6 8
 
0.2%
5 17
 
0.4%
4 21
 
0.5%
3 35
 
0.8%
2 48
 
1.1%
1 140
 
3.2%
0 4142
93.6%

Unemployment rate
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.566139
Minimum7.6
Maximum16.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-03-23T03:03:17.092525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile7.6
Q19.4
median11.1
Q313.9
95-th percentile16.2
Maximum16.2
Range8.6
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation2.6638505
Coefficient of variation (CV)0.23031458
Kurtosis-0.99552591
Mean11.566139
Median Absolute Deviation (MAD)1.7
Skewness0.21205105
Sum51168.6
Variance7.0960994
MonotonicityNot monotonic
2025-03-23T03:03:17.154583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7.6 571
12.9%
9.4 533
12.0%
10.8 525
11.9%
12.4 445
10.1%
12.7 419
9.5%
11.1 414
9.4%
15.5 397
9.0%
13.9 390
8.8%
8.9 368
8.3%
16.2 362
8.2%
ValueCountFrequency (%)
7.6 571
12.9%
8.9 368
8.3%
9.4 533
12.0%
10.8 525
11.9%
11.1 414
9.4%
12.4 445
10.1%
12.7 419
9.5%
13.9 390
8.8%
15.5 397
9.0%
16.2 362
8.2%
ValueCountFrequency (%)
16.2 362
8.2%
15.5 397
9.0%
13.9 390
8.8%
12.7 419
9.5%
12.4 445
10.1%
11.1 414
9.4%
10.8 525
11.9%
9.4 533
12.0%
8.9 368
8.3%
7.6 571
12.9%

Inflation rate
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2280289
Minimum-0.8
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative923
Negative (%)20.9%
Memory size34.7 KiB
2025-03-23T03:03:17.215647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.8
5-th percentile-0.8
Q10.3
median1.4
Q32.6
95-th percentile3.7
Maximum3.7
Range4.5
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.3827107
Coefficient of variation (CV)1.1259594
Kurtosis-1.0390334
Mean1.2280289
Median Absolute Deviation (MAD)1.2
Skewness0.25237535
Sum5432.8
Variance1.9118889
MonotonicityNot monotonic
2025-03-23T03:03:17.281484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1.4 893
20.2%
2.6 571
12.9%
-0.8 533
12.0%
0.5 445
10.1%
3.7 419
9.5%
0.6 414
9.4%
2.8 397
9.0%
-0.3 390
8.8%
0.3 362
8.2%
ValueCountFrequency (%)
-0.8 533
12.0%
-0.3 390
8.8%
0.3 362
8.2%
0.5 445
10.1%
0.6 414
9.4%
1.4 893
20.2%
2.6 571
12.9%
2.8 397
9.0%
3.7 419
9.5%
ValueCountFrequency (%)
3.7 419
9.5%
2.8 397
9.0%
2.6 571
12.9%
1.4 893
20.2%
0.6 414
9.4%
0.5 445
10.1%
0.3 362
8.2%
-0.3 390
8.8%
-0.8 533
12.0%

GDP
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0019688065
Minimum-4.06
Maximum3.51
Zeros0
Zeros (%)0.0%
Negative1711
Negative (%)38.7%
Memory size34.7 KiB
2025-03-23T03:03:17.346485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4.06
5-th percentile-4.06
Q1-1.7
median0.32
Q31.79
95-th percentile3.51
Maximum3.51
Range7.57
Interquartile range (IQR)3.49

Descriptive statistics

Standard deviation2.2699354
Coefficient of variation (CV)1152.95
Kurtosis-1.0016532
Mean0.0019688065
Median Absolute Deviation (MAD)1.47
Skewness-0.39406821
Sum8.71
Variance5.1526069
MonotonicityNot monotonic
2025-03-23T03:03:17.416274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.32 571
12.9%
-3.12 533
12.0%
1.74 525
11.9%
1.79 445
10.1%
-1.7 419
9.5%
2.02 414
9.4%
-4.06 397
9.0%
0.79 390
8.8%
3.51 368
8.3%
-0.92 362
8.2%
ValueCountFrequency (%)
-4.06 397
9.0%
-3.12 533
12.0%
-1.7 419
9.5%
-0.92 362
8.2%
0.32 571
12.9%
0.79 390
8.8%
1.74 525
11.9%
1.79 445
10.1%
2.02 414
9.4%
3.51 368
8.3%
ValueCountFrequency (%)
3.51 368
8.3%
2.02 414
9.4%
1.79 445
10.1%
1.74 525
11.9%
0.79 390
8.8%
0.32 571
12.9%
-0.92 362
8.2%
-1.7 419
9.5%
-3.12 533
12.0%
-4.06 397
9.0%

Target
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Graduate
2209 
Dropout
1421 
Enrolled
794 

Length

Max length8
Median length8
Mean length7.6787975
Min length7

Characters and Unicode

Total characters33971
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDropout
2nd rowGraduate
3rd rowDropout
4th rowGraduate
5th rowGraduate

Common Values

ValueCountFrequency (%)
Graduate 2209
49.9%
Dropout 1421
32.1%
Enrolled 794
 
17.9%

Length

2025-03-23T03:03:17.499967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T03:03:17.558079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
graduate 2209
49.9%
dropout 1421
32.1%
enrolled 794
 
17.9%

Most occurring characters

ValueCountFrequency (%)
r 4424
13.0%
a 4418
13.0%
o 3636
10.7%
u 3630
10.7%
t 3630
10.7%
d 3003
8.8%
e 3003
8.8%
G 2209
6.5%
l 1588
 
4.7%
D 1421
 
4.2%
Other values (3) 3009
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 4424
13.0%
a 4418
13.0%
o 3636
10.7%
u 3630
10.7%
t 3630
10.7%
d 3003
8.8%
e 3003
8.8%
G 2209
6.5%
l 1588
 
4.7%
D 1421
 
4.2%
Other values (3) 3009
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 4424
13.0%
a 4418
13.0%
o 3636
10.7%
u 3630
10.7%
t 3630
10.7%
d 3003
8.8%
e 3003
8.8%
G 2209
6.5%
l 1588
 
4.7%
D 1421
 
4.2%
Other values (3) 3009
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 4424
13.0%
a 4418
13.0%
o 3636
10.7%
u 3630
10.7%
t 3630
10.7%
d 3003
8.8%
e 3003
8.8%
G 2209
6.5%
l 1588
 
4.7%
D 1421
 
4.2%
Other values (3) 3009
8.9%

Interactions

2025-03-23T03:03:10.632093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:46.382822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:47.829871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:49.240892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:50.909284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:52.385169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:53.784450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:55.328788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:56.853106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:58.464552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:00.047703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:01.442771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:03.039711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:04.651145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:06.299506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:07.780795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:09.278087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:10.709251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:46.462940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:47.906874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:49.328282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:50.987617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:52.462936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:53.861533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:55.415634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:56.934684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:58.552850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:00.125676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:01.524697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:03.131039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:04.736140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:06.379066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:07.853869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-23T03:03:05.948805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:07.443731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:08.840892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:10.326676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:11.832454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:47.605061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:49.005909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:50.648381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:52.143804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:53.546832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:55.090800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:56.590446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:58.213073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:59.783536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:01.207816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:02.786468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:04.382888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:06.041346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:07.530810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:08.921771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:10.406431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:11.916667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:47.678890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:49.084185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:50.731451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:52.223870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:53.624821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:55.169400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:56.676557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:58.296463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:59.868064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:01.283976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:02.872558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:04.471268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:06.125919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:07.612279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:09.125346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:10.481219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:11.992215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:47.752695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:49.160043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:50.819483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:52.301354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:53.703579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:55.247891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:56.763261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:58.378287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:02:59.950814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:01.361055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:02.955134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:04.556977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:06.209418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:07.691336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:09.197991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T03:03:10.553601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-23T03:03:17.659729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Age at enrollmentApplication modeApplication orderCourseCurricular units 1st sem (approved)Curricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Daytime/evening attendanceDebtorDisplacedEducational special needsFather's occupationFather's qualificationGDPGenderInflation rateInternationalMarital statusMother's occupationMother's qualificationNacionalityPrevious qualificationScholarship holderTargetTuition fees up to dateUnemployment rate
Age at enrollment1.0000.288-0.3660.189-0.1660.2990.0010.171-0.2100.061-0.1880.301-0.0310.083-0.2130.0940.4890.1330.3890.0000.0620.133-0.0570.1620.0180.0000.3130.0840.1490.0000.1950.2120.2190.2090.019
Application mode0.2881.0000.1610.1680.1870.2670.1980.1430.1110.0580.2890.2320.2000.1280.1100.0370.4100.1640.4110.0000.0770.0940.1620.1770.1490.5300.2240.1060.0880.2340.4120.2200.2210.2020.176
Application order-0.3660.1611.0000.1430.090-0.2020.056-0.0960.074-0.0410.106-0.2010.075-0.0530.065-0.0300.1850.0900.3820.0000.0000.0380.0300.108-0.0110.0000.0700.0000.0330.0000.0860.0890.0790.073-0.104
Course0.1890.1680.1431.0000.2360.1170.4340.2410.2710.1010.2880.1160.4370.2450.2280.0820.9980.1520.3240.0620.0890.0870.1200.4240.0900.0930.1670.0890.0960.0400.1110.2220.2440.1390.123
Curricular units 1st sem (approved)-0.1660.1870.0900.2361.0000.3620.7070.2620.640-0.0690.8920.3670.6990.3640.663-0.0730.1730.1450.1290.0000.0000.0420.0590.237-0.0020.0000.0510.0560.0980.0000.1280.2640.4550.2800.074
Curricular units 1st sem (credited)0.2990.267-0.2020.1170.3621.0000.4230.3660.1010.1000.2890.9140.3760.3310.0930.0640.1710.0570.1070.0000.0230.0710.0220.027-0.0020.0000.0500.0380.0870.0000.1830.0860.0450.0000.024
Curricular units 1st sem (enrolled)0.0010.1980.0560.4340.7070.4231.0000.4200.363-0.0190.6530.4400.9620.4310.351-0.0210.2190.0470.1500.0430.0030.0490.0180.2130.0170.0000.0670.0340.0820.0000.1360.1560.1730.0830.108
Curricular units 1st sem (evaluations)0.1710.143-0.0960.2410.2620.3660.4201.0000.1120.2020.2520.3690.3860.6940.0900.1530.0630.0610.0980.0000.0000.077-0.0970.079-0.0400.0560.0320.0000.0320.1690.1030.1870.2580.1030.067
Curricular units 1st sem (grade)-0.2100.1110.0740.2710.6400.1010.3630.1121.000-0.0170.6290.0970.3680.1820.762-0.0470.1380.1060.0870.0000.0530.0520.0920.193-0.0370.0000.0390.0570.0650.0260.0930.1810.3830.2480.045
Curricular units 1st sem (without evaluations)0.0610.058-0.0410.101-0.0690.100-0.0190.202-0.0171.000-0.0550.059-0.0320.096-0.0410.3840.0190.0310.0000.0000.0000.097-0.1840.000-0.0680.0570.0550.0520.0650.1680.0760.0570.0620.078-0.067
Curricular units 2nd sem (approved)-0.1880.2890.1060.2880.8920.2890.6530.2520.629-0.0551.0000.3190.6740.3030.694-0.0640.1010.1800.1160.0000.0240.0350.0480.262-0.0150.0250.0480.0580.0920.0000.1180.2730.5160.3130.070
Curricular units 2nd sem (credited)0.3010.232-0.2010.1160.3670.9140.4400.3690.0970.0590.3191.0000.4140.3420.1030.0820.1820.0540.1280.0000.0000.0340.0240.028-0.0010.0000.0470.0270.1210.0000.1760.0750.0420.0000.012
Curricular units 2nd sem (enrolled)-0.0310.2000.0750.4370.6990.3760.9620.3860.368-0.0320.6740.4141.0000.4400.364-0.0270.1870.0750.1380.0000.0570.0370.0190.1620.0090.0300.0340.0370.1220.0000.1080.1130.1390.1240.139
Curricular units 2nd sem (evaluations)0.0830.128-0.0530.2450.3640.3310.4310.6940.1820.0960.3030.3420.4401.0000.1720.1590.1080.0590.0680.0290.0000.000-0.0040.109-0.0240.0110.0150.0000.0040.0800.1140.1660.2740.1240.061
Curricular units 2nd sem (grade)-0.2130.1100.0650.2280.6630.0930.3510.0900.762-0.0410.6940.1030.3640.1721.000-0.0520.0850.1500.0690.0000.0750.0490.1050.201-0.0430.0000.0330.0870.0600.0670.1040.1960.4550.2970.042
Curricular units 2nd sem (without evaluations)0.0940.037-0.0300.082-0.0730.064-0.0210.153-0.0470.384-0.0640.082-0.0270.159-0.0521.0000.0000.0630.0360.0000.0000.147-0.1110.055-0.0270.0000.0440.0000.0990.0000.0950.0400.0660.071-0.053
Daytime/evening attendance0.4890.4100.1850.9980.1730.1710.2190.0630.1380.0190.1010.1820.1870.1080.0850.0001.0000.0000.2510.0230.1260.2400.0930.0000.0730.0210.3660.1720.2740.0000.1950.0920.0780.0350.093
Debtor0.1330.1640.0900.1520.1450.0570.0470.0610.1060.0310.1800.0540.0750.0590.1500.0630.0001.0000.0880.0000.1380.0000.1380.0510.0860.0720.0300.1170.0350.0900.1440.0650.2410.4070.135
Displaced0.3890.4110.3820.3240.1290.1070.1500.0980.0870.0000.1160.1280.1380.0680.0690.0360.2510.0881.0000.0000.1120.1150.1400.1240.0580.0000.2750.1020.1440.0290.2210.0710.1120.0940.138
Educational special needs0.0000.0000.0000.0620.0000.0000.0430.0000.0000.0000.0000.0000.0000.0290.0000.0000.0230.0000.0001.0000.0000.0000.0570.0030.0420.0000.0000.0670.0230.0000.0000.0110.0000.0000.048
Father's occupation0.0620.0770.0000.0890.0000.0230.0030.0000.0530.0000.0240.0000.0570.0000.0750.0000.1260.1380.1120.0001.0000.1820.1610.0890.1270.0600.0000.5710.1890.1160.0500.2250.1400.0190.174
Father's qualification0.1330.0940.0380.0870.0420.0710.0490.0770.0520.0970.0350.0340.0370.0000.0490.1470.2400.0000.1150.0000.1821.0000.1210.0950.1090.0770.0980.1540.4310.0510.0980.1700.1340.0970.135
GDP-0.0570.1620.0300.1200.0590.0220.018-0.0970.092-0.1840.0480.0240.019-0.0040.105-0.1110.0930.1380.1400.0570.1610.1211.0000.081-0.1020.0650.0380.1630.1410.0410.1390.1290.0520.079-0.288
Gender0.1620.1770.1080.4240.2370.0270.2130.0790.1930.0000.2620.0280.1620.1090.2010.0550.0000.0510.1240.0030.0890.0950.0811.0000.0680.0200.0440.0380.0830.0290.1240.1680.2290.1020.076
Inflation rate0.0180.149-0.0110.090-0.002-0.0020.017-0.040-0.037-0.068-0.015-0.0010.009-0.024-0.043-0.0270.0730.0860.0580.0420.1270.109-0.1020.0681.0000.0300.0400.1290.1220.0170.1270.0980.0360.086-0.055
International0.0000.5300.0000.0930.0000.0000.0000.0560.0000.0570.0250.0000.0300.0110.0000.0000.0210.0720.0000.0000.0600.0770.0650.0200.0301.0000.0000.1720.1010.9980.0000.0220.0000.0390.069
Marital status0.3130.2240.0700.1670.0510.0500.0670.0320.0390.0550.0480.0470.0340.0150.0330.0440.3660.0300.2750.0000.0000.0980.0380.0440.0400.0001.0000.0570.1340.0000.1550.1040.0780.0930.044
Mother's occupation0.0840.1060.0000.0890.0560.0380.0340.0000.0570.0520.0580.0270.0370.0000.0870.0000.1720.1170.1020.0670.5710.1540.1630.0380.1290.1720.0571.0000.2490.2150.0630.1950.1610.0950.176
Mother's qualification0.1490.0880.0330.0960.0980.0870.0820.0320.0650.0650.0920.1210.1220.0040.0600.0990.2740.0350.1440.0230.1890.4310.1410.0830.1220.1010.1340.2491.0000.0000.0910.1820.1350.0560.156
Nacionality0.0000.2340.0000.0400.0000.0000.0000.1690.0260.1680.0000.0000.0000.0800.0670.0000.0000.0900.0290.0000.1160.0510.0410.0290.0170.9980.0000.2150.0001.0000.0000.0310.0260.0490.041
Previous qualification0.1950.4120.0860.1110.1280.1830.1360.1030.0930.0760.1180.1760.1080.1140.1040.0950.1950.1440.2210.0000.0500.0980.1390.1240.1270.0000.1550.0630.0910.0001.0000.1390.1460.1540.153
Scholarship holder0.2120.2200.0890.2220.2640.0860.1560.1870.1810.0570.2730.0750.1130.1660.1960.0400.0920.0650.0710.0110.2250.1700.1290.1680.0980.0220.1040.1950.1820.0310.1391.0000.3040.1360.130
Target0.2190.2210.0790.2440.4550.0450.1730.2580.3830.0620.5160.0420.1390.2740.4550.0660.0780.2410.1120.0000.1400.1340.0520.2290.0360.0000.0780.1610.1350.0260.1460.3041.0000.4310.053
Tuition fees up to date0.2090.2020.0730.1390.2800.0000.0830.1030.2480.0780.3130.0000.1240.1240.2970.0710.0350.4070.0940.0000.0190.0970.0790.1020.0860.0390.0930.0950.0560.0490.1540.1360.4311.0000.091
Unemployment rate0.0190.176-0.1040.1230.0740.0240.1080.0670.045-0.0670.0700.0120.1390.0610.042-0.0530.0930.1350.1380.0480.1740.135-0.2880.076-0.0550.0690.0440.1760.1560.0410.1530.1300.0530.0911.000

Missing values

2025-03-23T03:03:12.173989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-23T03:03:12.568382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Marital statusApplication modeApplication orderCourseDaytime/evening attendancePrevious qualificationNacionalityMother's qualificationFather's qualificationMother's occupationFather's occupationDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentInternationalCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPTarget
0Single2nd phase - general contingent5Animation and Multimedia DesignDaytimeSecondary educationPortugueseBasic Education 3rd Cycle (9th/10th/11th Year) or Equiv.Other - 11th Year of SchoolingPersonal Services, Security and Safety Workers and SellersUnskilled Workers10011020000000.000000000000.000000010.81.41.74Dropout
1SingleInternational student (bachelor)1TourismDaytimeSecondary educationPortugueseSecondary Education - 12th Year of Schooling or Eq.Higher Education - DegreeIntermediate Level Technicians and ProfessionsIntermediate Level Technicians and Professions100010190066614.0000000066613.666667013.9-0.30.79Graduate
2Single1st phase - general contingent5Communication DesignDaytimeSecondary educationPortugueseBasic education 1st cycle (4th/5th year) or equiv.Basic education 1st cycle (4th/5th year) or equiv.Unskilled WorkersUnskilled Workers10001019006000.000000006000.000000010.81.41.74Dropout
3Single2nd phase - general contingent2Journalism and CommunicationDaytimeSecondary educationPortugueseBasic Education 2nd Cycle (6th/7th/8th Year) or Equiv.Basic education 1st cycle (4th/5th year) or equiv.Personal Services, Security and Safety Workers and SellersIntermediate Level Technicians and Professions100100200068613.42857100610512.40000009.4-0.8-3.12Graduate
4MarriedOver 23 years old1Social Service (evening attendance)EveningSecondary educationPortugueseBasic education 1st cycle (4th/5th year) or equiv.Basic Education 2nd Cycle (6th/7th/8th Year) or Equiv.Unskilled WorkersUnskilled Workers000100450069512.3333330066613.000000013.9-0.30.79Graduate
5MarriedOver 23 years old1Management (evening attendance)EveningBasic education 3rd cycle (9th/10th/11th year) or equiv.PortugueseBasic education 1st cycle (4th/5th year) or equiv.Basic education 1st cycle (4th/5th year) or equiv.Unskilled WorkersSkilled Workers in Industry, Construction and Craftsmen0011105000510511.85714300517511.500000516.20.3-0.92Graduate
6Single1st phase - general contingent1NursingDaytimeSecondary educationPortugueseBasic Education 3rd Cycle (9th/10th/11th Year) or Equiv.Basic Education 2nd Cycle (6th/7th/8th Year) or Equiv.Skilled Workers in Industry, Construction and CraftsmenArmed Forces Professions100101180079713.3000000088814.345000015.52.8-4.06Graduate
7Single3rd phase - general contingent4TourismDaytimeSecondary educationPortugueseBasic education 1st cycle (4th/5th year) or equiv.Basic education 1st cycle (4th/5th year) or equiv.Unskilled WorkersUnskilled Workers10001022005500.000000005500.000000015.52.8-4.06Dropout
8Single1st phase - general contingent3Social ServiceDaytimeSecondary educationRomanianSecondary Education - 12th Year of Schooling or Eq.Secondary Education - 12th Year of Schooling or Eq.Unskilled WorkersUnskilled Workers000101211068613.8750000067614.142857016.20.3-0.92Graduate
9Single1st phase - general contingent1Social ServiceDaytimeSecondary educationPortugueseSecondary Education - 12th Year of Schooling or Eq.Basic Education 3rd Cycle (9th/10th/11th Year) or Equiv.Administrative staffSkilled Workers in Industry, Construction and Craftsmen101000180069511.40000000614213.50000008.91.43.51Dropout
Marital statusApplication modeApplication orderCourseDaytime/evening attendancePrevious qualificationNacionalityMother's qualificationFather's qualificationMother's occupationFather's occupationDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentInternationalCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPTarget
4414Single1st phase - general contingent1EquincultureDaytimeSecondary educationPortugueseHigher Education - DegreeBasic Education 2nd Cycle (6th/7th/8th Year) or Equiv.Intermediate Level Technicians and ProfessionsPersonal Services, Security and Safety Workers and Sellers100100180056511.8000001058511.60000009.4-0.8-3.12Graduate
4415DivorcedOver 23 years old1NursingDaytimeBasic education 3rd cycle (9th/10th/11th year) or equiv.PortugueseBasic education 1st cycle (4th/5th year) or equiv.Basic education 1st cycle (4th/5th year) or equiv.Farmers and Skilled Workers in Agriculture, Fisheries and ForestryFarmers and Skilled Workers in Agriculture, Fisheries and Forestry0010004600714312.33333300712311.083333011.10.62.02Dropout
4416SingleChange of course2NursingDaytimeSecondary educationPortugueseBasic Education 2nd Cycle (6th/7th/8th Year) or Equiv.Basic Education 2nd Cycle (6th/7th/8th Year) or Equiv.Unskilled WorkersPersonal Services, Security and Safety Workers and Sellers0001002301114151212.62500011114151212.62500017.62.60.32Graduate
4417Single1st phase - general contingent1Communication DesignDaytimeSecondary educationPortugueseSecondary Education - 12th Year of Schooling or Eq.Secondary Education - 12th Year of Schooling or Eq.Unskilled WorkersUnskilled Workers100101200066613.8333330066613.500000016.20.3-0.92Graduate
4418SingleTechnological specialization diploma holders1Communication DesignDaytimeTechnological specialization coursePortugueseHigher Education - DegreeBasic Education 2nd Cycle (6th/7th/8th Year) or Equiv.Intermediate Level Technicians and ProfessionsUnskilled Workers000110200277612.50000005910713.142857116.20.3-0.92Graduate
4419Single1st phase - general contingent6Journalism and CommunicationDaytimeSecondary educationPortugueseSecondary Education - 12th Year of Schooling or Eq.Secondary Education - 12th Year of Schooling or Eq.Personal Services, Security and Safety Workers and SellersAdministrative staff000110190067513.6000000068512.666667015.52.8-4.06Graduate
4420Single1st phase - general contingent2Journalism and CommunicationDaytimeSecondary educationRussianSecondary Education - 12th Year of Schooling or Eq.Secondary Education - 12th Year of Schooling or Eq.Unskilled WorkersUnskilled Workers101000181066612.0000000066211.000000011.10.62.02Dropout
4421Single1st phase - general contingent1NursingDaytimeSecondary educationPortugueseBasic education 1st cycle (4th/5th year) or equiv.Basic education 1st cycle (4th/5th year) or equiv.Unskilled WorkersUnskilled Workers100101300078714.9125000089113.500000013.9-0.30.79Dropout
4422Single1st phase - general contingent1ManagementDaytimeSecondary educationPortugueseBasic education 1st cycle (4th/5th year) or equiv.Basic education 1st cycle (4th/5th year) or equiv.Skilled Workers in Industry, Construction and CraftsmenAdministrative staff100101200055513.8000000056512.00000009.4-0.8-3.12Graduate
4423SingleOrdinance No. 854-B/991Journalism and CommunicationDaytimeSecondary educationCape VerdeanBasic Education 2nd Cycle (6th/7th/8th Year) or Equiv.Basic education 1st cycle (4th/5th year) or equiv.Personal Services, Security and Safety Workers and SellersUnskilled Workers100100221068611.6666670066613.000000012.73.7-1.70Graduate